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This paper presents a novel ionospheric phase decontamination algorithm based on sparse reconstruction.
In this paper, a new ionospheric phase decontamination algorithm is proposed based on sparse reconstruction.
Section 3 will present the proposed decontamination algorithm based on sparse reconstruction.
However, there has been little work in SAR imaging based on sparse reconstruction combined with tensor decomposition.
On the basis of above considerations, this paper gives a high-accuracy ionospheric phase decontamination algorithm based on sparse reconstruction.
Considering that the ionospheric frequency modulation function is sparse in the time-frequency domain and the sparse signal reconstruction method has the high estimation precision [19], we propose a novel phase compensation algorithm based on sparse reconstruction.
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In the last decade, DOA estimation algorithms based on sparse signal reconstruction (SSR) were proposed.
Therefore we will exploit target priors and estimate the model based on sparse signal reconstruction.
Array signal processing algorithms based on sparse signal reconstruction are proposed for a low-cost single-channel DBF array.
An array signal recovery algorithm based on sparse signal reconstruction theory is proposed for a single-RF-channel digital beamforming (DBF) array.
A motion parameter estimation method based on sparse signal reconstruction has recently been developed [2], which coherently combines data from all available illuminators of opportunity and, thereby, achieves overall signal enhancement and multi-static diversity.
More suggestions(11)
based on sparse employment
based on sparse linear
based on sparse discriminant
based on sparse representation
based on dynamic reconstruction
based on sparse learning
based on local reconstruction
based on sparse concentration
based on sparse signal
based on iterative reconstruction
based on palaeoecological reconstruction
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